Data Scientist - Hardware Acoustics

The Hardware Acoustic organization at Apple is dedicated to delivering industry-leading audio experiences across all our products. We are a multidisciplinary team of acoustic engineers, researchers, and machine learning experts who push the boundaries of sound quality, noise cancellation, and user interaction.\\n\\nOur Machine Learning Data team is the foundational backbone for these efforts. We are responsible for building the robust data infrastructure, pipelines, and analytical tools that enable the development, training, and evaluation of cutting-edge machine learning models for acoustic applications. We work with vast, complex datasets, ensuring their quality, accessibility, and utility for our ML scientists and engineers.

We are seeking a highly motivated and skilled Data Scientist/Engineer to join our Machine Learning Data team within Hardware Acoustics. This role sits at the intersection of data engineering, data science, and machine learning, with a specific focus on acoustic and sensor data.\n\nYou will be instrumental in designing, developing, and maintaining scalable data pipelines, ensuring data quality, and preparing complex datasets that power machine learning models enhancing Apple's hardware acoustic performance. You will collaborate closely with ML engineers, acoustic scientists, and hardware engineers to understand their data needs and deliver impactful, data-driven solutions.

Bachelor's or Master's degree in Computer Science, Electrical Engineering, Data Science, or a related quantitative field.\n3+ years of professional experience in data engineering, data science, or machine learning engineering roles, with a strong focus on data pipelines and data preparation.\nExpert proficiency in Python for data manipulation, scripting, and automation.\nStrong SQL skills for complex data querying, analysis, and database management.\nExperience with distributed data processing frameworks (e.g., Apache Spark, Hadoop).\nSolid understanding of data warehousing concepts, data modeling, and ETL/ELT principles.\nExperience with version control systems (e.g., Git).

Experience working with acoustic data, audio signal processing, or sensor data.\nFamiliarity with machine learning concepts and experience using ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).\nExperience with MLOps principles and practices for managing the ML lifecycle.\nKnowledge of data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn).\nExperience with real-time data processing or streaming technologies.\nExcellent problem-solving, analytical, and communication skills, with the ability to explain complex data concepts to diverse audiences.\nFamiliarity with web front/back end.\nFamiliarity with large-scale data platforms and services (e.g., cloud-based data warehouses/lakes or similar internal infrastructure).